Abstract
Potential field data have long been used in geophysical exploration for archeological, mineral, and reservoir targets. For all these targets, the increased search of highly detailed three-dimensional subsurface volumes has also promoted the recollection of high-density contrast data sets. While there are several approaches to handle these large-scale inverse problems, most of them rely on either the extensive use of high-performance computing architectures or data-model compression strategies that may sacrifice some level of model resolution. We posit that the superposition and convolutional properties of the potential fields can be easily used to compress the information needed for data inversion and also to reduce significantly redundant mathematical computations. For this, we developed a convolution-based conjugate gradient 3D inversion algorithm for the most common types of potential field data. We demonstrate the performance of the algorithm using a resolution test and a synthetic experiment. We then apply our algorithm to gravity and magnetic data for a geothermal prospect in the Acoculco caldera in Mexico. The resulting three-dimensional model meaningfully determined the distribution of the existent volcanic infill in the caldera as well as the interrelation of various intrusions in the basement of the area. We propose that these intrusive bodies play an important role either as a low-permeability host of the heated fluid or as the heat source for the potential development of an enhanced geothermal system.
Highlights
Potential field data such as gravity and magnetics are among the first geophysical data used in mineral and hydrocarbon exploration
We propose that given the regular accommodation of both large spatial data grids and discretized three-dimensional volumes, we can take the advantage of the superposition principle inherent to potential fields as well as the convolution-based property of their associated integral equations to establish a general framework for an exact sensitivity matrix compression useful for an efficient 3D inversion of potential field data such as gravity, magnetics, and gravity gradient data
We have developed a convolution-based conjugate gradient algorithm for the inversion of potential field data to produce three-dimensional volumes of density or magnetization contrasts
Summary
Potential field data such as gravity and magnetics are among the first geophysical data used in mineral and hydrocarbon exploration. One example is the development of modern airborne gravity gradiometers (Zhdanov et al, 2004; Nabighian et al, 2005; Dransfield and Zeng, 2009; Jekeli, 2006) and the increased use of unmanned aerial vehicles for aeromagnetic surveys (e.g., Aleshin et al, 2020; Jiang et al, 2020; Parshin et al, 2020; Walter et al, 2020) These technological developments have yielded the assimilation of the larger potential field data sets needed to achieve higher subsurface detail for reservoir, mineral, and archaeological studies. We note that potential fields follow the same principles: they are conservative (i.e., result on harmonic fields that can be described by a scalar field) and depend on the relative position between the source and the measurement point Both features are fundamental for Fourier transform processing and inversion approaches that have long been in place (e.g., Parker and Huestis, 1974). We apply our algorithm to gravity and magnetic data from the Acoculco geothermal zone in Mexico
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